Source:  Gerasim@home
samedi 6 janvier 2018 15:12

В проекте запущен тестовый подпроект, целью которого является тестирование возможностей поиска ОДЛК на NVidia GPU. Подробности обсуждаются тут:,


The project launches a test subproject aimed to testing of ODLS searching ability using NVidia GPUs. Detailed description here:,

Aliquot sequence 1164432 has terminated!!!
Source:  YAFU
samedi 6 janvier 2018 08:34

Validation Inconclusive Errors
Source:  Milkyway@home
samedi 6 janvier 2018 03:42

Hi all,

There has been some reports of validation inconclusive issues occurring with the Nbody workunits. It seems to be isolated to the nbody app. I believe this coincided with a database issue on our end. I am working on resolving the issue.

Thank you for you patience and continued support,

Siever for aarch64
Source:  yoyo@home
samedi 6 janvier 2018 00:00

The Siever app is now also available for 64 bit Linux systems running on ARM.

Perfect Cuboid: New Version
Source:  yoyo@home
samedi 6 janvier 2018 00:00

Perfect Cuboid continues with a new version for 64 bit Windows, Linux and ARM. We are checking now up to 251.

Planned Maintenance on Saturday, January 6 (Completed)
Source:  World Community Grid News and Updates
jeudi 4 janvier 2018 21:54

We are updating the operating system on our servers on Saturday, January 6, beginning at 2:00 UTC.

Projects are (no longer) down
Source:  SETI@Home
jeudi 4 janvier 2018 21:04

Our upload server is malfunctioning. The projects are down until we can fix this.

New Student and QMML Project
Source:  GPUGrid
jeudi 4 janvier 2018 14:44

Hello everyone and happy new year! My name is Dominik and I am a masters student from Germany. I studied biochemistry in the city of Bayreuth, but I always focused my work more towards biophysics and bioinformatics. I'm in the group since October, where we have since then started to initiate a quantum chemical machine learning project. We soon came to the conclusion that we need a quantum chemical database in order to drive research forward in this field. This is where we need your help! Some of you have already seen that I released a small amount of 50.000 WUs yesterday evening and there is even more. The WUs contain a certain amount of small molecules for which quantum chemical simulations will be performed. Depending on the size of the molecule the simulation (and the WU) will take longer. We did initial benchmarks to estimate how to design the WUs, but everything turned out a bit different with the very long WUs I prepared for Toni which you were fighting with over the last month. Thanks to everyone who was helping and sticking through this! I will try to adjust the WUs as best as I can, but for now the current DOMINIK WUs that are already on the server are fixed. Regarding the windows based QM tasks I cannot give a clear answer about this yet, but it's definitely one of our goals. It is a long term goal of ours to keep running the quantum chemistry tasks on GPUGRID and I try my best to ensure that there are always tasks available. So thanks again to everyone who is participating and helping out. I really appreciate it very much. Have a great start into 2018!

Dr Sihan Li presents at AGU Fall Meeting
mercredi 3 janvier 2018 17:40

The world’s largest Earth and space science meeting – the AGU Fall Meeting – took place in New Orleans, Louisiana, from 11-15 December. Post-doctoral Research Associate Dr Sihan Li (Meredith) attended the meeting to give several presentations on behalf of the project.

Her presentation Changing frequency of flooding in Bangladesh: Is the wettest place on Earth getting wetter? (Karsten Haustein, Peter Uhe, Ruksana Rimi, Akm Saiful Islam, Friederike Otto) in the session entitled ‘Improving Our Mechanistic Understanding of the Regional Climate Response to Anthropogenic Aerosols’, presented results from an analysis of extreme precipitation that led to the Bangladesh floods in summer 2016 (see also the REBuILD project).

Human influence on the Asian monsoon is exerted by two counteracting forces, anthropogenic warming due to the influence of increasing Greenhouse Gas (GHG) emissions, and radiative cooling due to increased amounts of anthropogenic aerosols. GHG emissions tend to intensify the water cycle and increase monsoon precipitation, whereas aerosols are considered to have the opposite effect.

In reality we are essentially committed to more rainfall extremes already as aerosol pollution will eventually be reduced regardless of future GHG emissions. Therefore it is crucial to assess the risk related to removing anthropogenic aerosols from the current world as opposed to standard experiments that use projected climate scenarios.

In the Session on ‘Novel Methods for Combing Physical Simulation, Machine Learning, and Data-Driven Analysis in Climate Studies and Geophysical Sciences’, Dr Li presented Using Perturbed Physics Ensembles and Machine Learning to Select Parameters for Reducing Regional Biases in a Global Climate Model (Sihan Li, David E Rupp, Linnia Hawkins, Philip Mote, Doug J McNeall, Sarah Sparrow, David Wallom, Richard Betts).

The study investigates the potential to reduce known summer hot/dry biases over Pacific Northwest in the UK Met Office’s atmospheric model (HadAM3P) by simultaneously varying multiple model parameters. Results illustrate the potential of using machine learning to train cheap and fast statistical emulators of climate models.

Finally, Changing Drought Risk In a Warming World- using event attribution methods to explore changing likelihoods of drought in east Africa in the past, present and future (Sarah O’Keefe, Sihan Li, Friederike Otto) in the session ‘Climate Extremes: Patterns, Mechanisms, and Attribution’, estimated current and future changes in the probability of drought in different East African regions, making use of the HAPPI project in which large ensembles of atmosphere-only models are run under historic, 1.5 and 2 degrees C conditions (Mitchell et al, 2017).

East Africa is particularly vulnerable to potential impacts of anthropogenic climate change, due to the particular climatic forces at play in the region and the population’s dependence on rain fed agriculture. However large natural inter-annual variability in the region has made the detection and attribution of anthropogenic forcing a challenge. The large ensemble multi-model framework in the HAPPI design allows for a more robust estimation of extremes than ever before.

Happy New Year from Einstein@Home!
Source:  Einstein@Home
mercredi 3 janvier 2018 15:02

Dear Volunteers,

I want to thank you for your continuing contributions to Einstein@Home, and to wish all of you a healthy and happy 2018.

The past year has seen several important steps forward, including publication of the first results from Einstein@Home searches in
advanced LIGO data, and the discovery of many new gamma ray pulsars in data from the Fermi satellite.

read more